Atrial fibrillation patient preferences for oral anticoagulation and stroke knowledge: Results of a conjoint analysis
Bibliographic record
Abstract
BACKGROUND: Guidelines recommend that patients with atrial fibrillation (AF) are involved in oral anticoagulant (OAC) treatment decisions. Understanding which OAC attributes AF patients value most could help optimize treatment. OBJECTIVE: To assess the relationship between patient's stroke knowledge and their preferences for specific OAC attributes. METHODS: A cross-sectional online survey was conducted in patients with nonvalvular AF taking an OAC for stroke prevention in the United States, Canada, Germany, France, and Japan. Patients were asked about their stroke knowledge, perception of the seriousness of AF and concern about stroke, and to rank 7 OAC attributes in order of importance. A conjoint analysis was performed to determine the inherent value of 4 attributes. RESULTS: In total, 937 patients (mean age [standard deviation] 54.3 [16.6] years; 37.1% female) participated. Of these, 19.5%, 27.9%, and 29.8% had good, moderate, and low stroke knowledge, respectively; 22.8% had no stroke knowledge. Overall, 39.4% of patients (47.5% with good stroke knowledge) perceived AF as very/extremely serious. The OAC attribute ranked as most important was stroke prevention followed by major bleeding risk, other side effects, dosing frequency, antidote availability, dietary restrictions, and use with/without food. In the conjoint analysis, stroke risk reduction was the most valued property, followed by reduction in major bleeding risk, less frequent administration, and administration with/without food. Preferences did not differ with level of stroke knowledge, perception of seriousness of AF, concern of stroke, or medication burden. CONCLUSIONS: Most AF patients consider efficacy and safety to be the most important OAC attributes, whereas dosing frequency was deemed as less important.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".